At a high-level, the podcast covers our understanding of RAG systems through 4 key areas: (1) Ingest / ETL, (2) Search, (3) Generate / Agents, and (4) Evaluation.
Discussing these lead to all sorts of topics from Knowledge Graph RAG, to Function Calling and Tool Selection, Re-ranking, Quantization, and many more!
This discussion forced me to re-think many of my previously held beliefs about the current RAG stack, particularly the definition of \u201cAgents\u201d. I came in believing that the best way of viewing \u201cAgents\u201d is an abstraction on top of multiple pipelines, such as an \u201cEmail Agent\u201d, but Kyle presented the idea of looking at \u201cAgents\u201d as scoping the tools each LLM call is connected to, such as `read_email` or `calculator`. Would love to know what people think about this one, as I think getting a consensus definition of \u201cAgents\u201d can clarify a lot of the current confusion for people building with LLMs / Generative AI.
I hope you find the podcast useful, this was such a fun one! Thank you so much for joining Kyle!
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At a high-level, the podcast covers our understanding of RAG systems through 4 key areas: (1) Ingest / ETL, (2) Search, (3) Generate / Agents, and (4) Evaluation.
Discussing these lead to all sorts of topics from Knowledge Graph RAG, to Function Calling and Tool Selection, Re-ranking, Quantization, and many more!
This discussion forced me to re-think many of my previously held beliefs about the current RAG stack, particularly the definition of \u201cAgents\u201d. I came in believing that the best way of viewing \u201cAgents\u201d is an abstraction on top of multiple pipelines, such as an \u201cEmail Agent\u201d, but Kyle presented the idea of looking at \u201cAgents\u201d as scoping the tools each LLM call is connected to, such as `read_email` or `calculator`. Would love to know what people think about this one, as I think getting a consensus definition of \u201cAgents\u201d can clarify a lot of the current confusion for people building with LLMs / Generative AI.
I hope you find the podcast useful, this was such a fun one! Thank you so much for joining Kyle!
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I am SUPER EXCITED to publish the 116th episode of the Weaviate Podcast featuring Matt Biilmann from Netlfiy and Sebastian Witalec and Charles Pierse from Weaviate!
This podcast explores Agent Experience -- how do we design software not only for human users, but AI systems as well? How do we write documentation for Agents differently than Humans? How do we design REST or gRPC APIs, or programming languages clients, for Agents differently than Humans? llms.txt, JSON tool definitions, agents to agent, breaking changes, \u2026 there were so many interesting topics explored in this podcast!
I really hope you find it useful! As always more than happy to discuss these ideas further with you!
YouTube: https://www.youtube.com/watch?v=MAE3I8O_w84
Spotify: https://creators.spotify.com/pod/show/weaviate/episodes/Agent-Experience-with-Matt-Biilmann--Sebastian-Witalec--and-Charles-Pierse---Weaviate-Podcast-116-e2vfbqq"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["weaviate"],"value":"Agent Experience \u2013 Weaviate Podcast #116"}},"_tags":["story","author_CShorten","story_43195265","ask_hn"],"author":"CShorten","children":[43195310,43197508],"created_at":"2025-02-27T15:31:35Z","created_at_i":1740670295,"num_comments":2,"objectID":"43195265","points":5,"story_id":43195265,"story_text":"You have probably heard about how you can connect LLMs to external software tools, in systems mostly referred to as \u201cAgents\u201d. As software developers, we now not only have to think about the Human Experience of interacting and learning to use our tools, but also Agent Experience!
I am SUPER EXCITED to publish the 116th episode of the Weaviate Podcast featuring Matt Biilmann from Netlfiy and Sebastian Witalec and Charles Pierse from Weaviate!
This podcast explores Agent Experience -- how do we design software not only for human users, but AI systems as well? How do we write documentation for Agents differently than Humans? How do we design REST or gRPC APIs, or programming languages clients, for Agents differently than Humans? llms.txt, JSON tool definitions, agents to agent, breaking changes, \u2026 there were so many interesting topics explored in this podcast!
I really hope you find it useful! As always more than happy to discuss these ideas further with you!
YouTube: https://www.youtube.com/watch?v=MAE3I8O_w84
Spotify: https://creators.spotify.com/pod/show/weaviate/episodes/Agent-Experience-with-Matt-Biilmann--Sebastian-Witalec--and-Charles-Pierse---Weaviate-Podcast-116-e2vfbqq","title":"Agent Experience \u2013 Weaviate Podcast #116","updated_at":"2025-03-03T19:23:42Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"sourcelabs"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["weaviate"],"value":"Adding Intelligent Search to a Rails Application with Weaviate"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://medium.com/@rushing_andrei/adding-intelligent-search-to-a-rails-application-56d3ed794b8a"}},"_tags":["story","author_sourcelabs","story_35718397"],"author":"sourcelabs","children":[35718581],"created_at":"2023-04-26T18:58:59Z","created_at_i":1682535539,"num_comments":2,"objectID":"35718397","points":5,"story_id":35718397,"title":"Adding Intelligent Search to a Rails Application with Weaviate","updated_at":"2024-09-20T13:56:57Z","url":"https://medium.com/@rushing_andrei/adding-intelligent-search-to-a-rails-application-56d3ed794b8a"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"TalktoCrystal"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["weaviate"],"value":"Open source db platform for vector db engines, qdrant, milvus, weaviate all in 1"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/apecloud/kubeblocks/blob/main/docs/user_docs/kubeblocks-for-vector-database/manage-vector-databases.md"}},"_tags":["story","author_TalktoCrystal","story_38840179"],"author":"TalktoCrystal","children":[38840328],"created_at":"2024-01-02T10:31:20Z","created_at_i":1704191480,"num_comments":1,"objectID":"38840179","points":5,"story_id":38840179,"title":"Open source db platform for vector db engines, qdrant, milvus, weaviate all in 1","updated_at":"2024-09-20T16:02:11Z","url":"https://github.com/apecloud/kubeblocks/blob/main/docs/user_docs/kubeblocks-for-vector-database/manage-vector-databases.md"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"thirdtrigger"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["weaviate"],"value":"Weaviate is an open-source search engine powered by ML, vectors, and GraphQL"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["weaviate"],"value":"https://www.zdnet.com/article/weaviate-an-open-source-search-engine-powered-by-machine-learning-vectors-graphs-and-graphql/"}},"_tags":["story","author_thirdtrigger","story_26737196"],"author":"thirdtrigger","children":[26737741],"created_at":"2021-04-08T10:43:40Z","created_at_i":1617878620,"num_comments":1,"objectID":"26737196","points":5,"story_id":26737196,"title":"Weaviate is an open-source search engine powered by ML, vectors, and GraphQL","updated_at":"2024-09-20T08:19:34Z","url":"https://www.zdnet.com/article/weaviate-an-open-source-search-engine-powered-by-machine-learning-vectors-graphs-and-graphql/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"CShorten"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Open-Source LLMs are currently one of the biggest trends in AI to be preparing for! Vinod has a remarkable history with open-source in software, having famously published the "Halloween Documents", a set of internal strategy memos at Microsoft explaining the oncoming wave of open-source. Vinod shares some fascinating insights on how building business around open-source has evolved and then Bob sparks the debate around "stateless" models.
Bob has been intensely exploring the new business philosophy around AI models, making connections to traditional ways of thinking about stateless and stateful software. For example, an mp3 file of a song is stateless, whereas say, your playlist, and the application that serves it, is stateful. AI models sit at an interesting intersection of this, the model itself is stateless, but once you connect it to your data with RAG (Retrieval-Augmented Generation), for example, you make it stateful. The podcast discusses this viewpoint and the nuances of it. Vinod pushes back on the analogy, preferring to describe the models as "pre-baked". I think this is such an interesting topic on the future of value capture in AI models, curious what people think!
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I hope this quick synopsis inspires your interest in the podcast! There are many other topics covered such as MemGPT and Gorilla, DSPy, and Generative Feedback Loops!
YouTube: https://www.youtube.com/watch?v=ySNX2cPh5Tk
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I hope this quick synopsis inspires your interest in the podcast! There are many other topics covered such as MemGPT and Gorilla, DSPy, and Generative Feedback Loops!
YouTube: https://www.youtube.com/watch?v=ySNX2cPh5Tk
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